Find research datasets worth reusing

opencc-by-4.0Jan 2015View details →

Fig. 2 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 2: Flowchart of steps and methods followed (AHP: Analytic Hierarchy Process, FM: Fuzzy Membership).

opencc-by-4.0Jan 2015View details →

Fig. 7 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 7: Spatial representation of the Fishing pressure index from the small scale coastal fishery (FPc).

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Data from: A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation

<div> Understanding how plant phenotypes are shaped by their environments is crucial for addressing questions about crop adaptation to new environments. This study investigated the interplay between developmental responses to temperature fluctuations and photoperiod perception in maize that contribute to genotype-by-environment variation in flowering time. We present a physiological reaction norm for flowering time plasticity (PRN-FTP) for studying large collections of genotypes tested in multi-environment trial (MET) networks. Using a new variable for computational envirotyping of sensed photoperiod, it was found that, at high latitudes, different genotypes in the same environment can experience hours-long differences in photoperiod. This emphasizes the importance of considering genotype-specific differences in the experienced environment when investigating plasticity. A statistical framework is introduced for modeling the PRN-FTP as a non-linear response function, with parameters putatively linked to different regulatory modules for flowering time. Applying the PRN-FTP to a sample of global breeding material for maize showed that tropical and temperate maize occupy distinct territories of the trait space for PRN-FTP parameters, supporting that the geographical spread and adaptation of maize was differentially mediated by exogenous and endogenous pathways for flowering time regulation. Our results have implications for understanding crop adaptation and for future crop improvement efforts. </div>

opencc-zeroJul 2024View details →

Fig. 1 in Large theropod dinosaur footprint associations in western Gondwana: Behavioural and palaeogeographic implications

Fig. 1. Location of the large theropod trackbeds: track 1 indicates the Querulpa Chico locality while track 2 indicates the Chacarilla locality.

Fig. 5 in Large theropod dinosaur footprint associations in western Gondwana: Behavioural and palaeogeographic implications

Fig. 5. Photographs of Early Cretaceous theropod footprints from the Querulpa Chico tracksite, Peru. A–I refer to the individual trackways in Fig. 4, and the number to the particular print in the trackway. Arrows point to hallux impressions. Scale bars 0.5 m.

Fig. 6. Early Cretaceous Chacarilla tracksite, Chile. A in Large theropod dinosaur footprint associations in western Gondwana: Behavioural and palaeogeographic implications

Fig. 6. Early Cretaceous Chacarilla tracksite, Chile. A. Line drawing and photographs of the Chacarilla theropod tracksite, showing orientations and distribution of trackways. B. Schematic map of trackways 3 and 4, crossed perpendicularly by trackways 1, 5 and 2 (unidentified trackway). Scale bars in A 5 m.

Fig. 3 in Large theropod dinosaur footprint associations in western Gondwana: Behavioural and palaeogeographic implications

Fig. 3. Measurements taken in situ on footprints and trackways. Anteroposterior track length: distance between the distal tip of digit III and the proximal boundary of the sole; mediolateral track width: distance between the distal tip of lateral digits measured perpendicular to the track axis; pace angle: angle formed by the two segments joining three consecutive tracks; pace, distance between two consecutive tracks; stride length: distance between two consecutive tracks on the same side (left or right) of the trackway.

Fig. 8 in Toward the origin of amniotes: Diadectomorph and synapsid footprints from the early Late Carboniferous of Germany

Fig. 8. Stratocladogram of tetrapod phylogeny. Although extending the known body fossil record, the stratigraphic position of the diadectomorph and synapsid footprints from the Bochum Formation of western Germany fits well the phylogenetic pattern. Black bars indicate the known record of skeletal remains (Reisz 1986; Kissel and Reisz 2004a, b). Cladogram topology and minimum times of divergence are based on Kissel and Reisz (2004a, b), Müller and Reisz (2004), and Reisz (2007). The chronostratigraphic scale is adopted from Menning (2005).

Fig. 7 in Toward the origin of amniotes: Diadectomorph and synapsid footprints from the early Late Carboniferous of Germany

Fig. 7. Comparison of trackway pattern and selected parameters of Dimetropus sp. from the Late Carboniferous Bochum Formation, Ruhr area, Germany, DBM 060003260001 to DBM 060003260005 (A) and Dimetropus leisnerianus from the Early Permian Tambach Formation, Thuringian Forest, Germany, MNG 1762 (B), MNG 1828 (C), MNG 13490 (D). E, F. Derived trackway and imprint parameters graphically expressing the mean, minimum, and maximum values. G, H. Relative lengths of the manus and pes imprint digit lengths expressed as a percentage of the length of digit IV. Data source for E–H, see Appendix 4.

Fig. 3 in Toward the origin of amniotes: Diadectomorph and synapsid footprints from the early Late Carboniferous of Germany

Fig. 3. Undertrack preservation in Ichniotherium tetrapod footprints. A. Ichniotherium praesidentis (Schmidt, 1956) from the Late Carboniferous Bochum Formation, Ruhr area, Germany; DBM 060003309003. B–D. Ichniotherium cottae (Pohlig, 1885) from Early Permian strata of the Thuringian Forest area, Germany; MNG 2049 (B), MNG 1871 (C), and MNG 2016 (D). Semicircularly arranged spherical imprints of the digit tips of the digits I–III or I–IV, which are typical in appearance to the undertrack preservation of Ichniotherium. Scale bars 100 mm.

Fig. 6 in Toward the origin of amniotes: Diadectomorph and synapsid footprints from the early Late Carboniferous of Germany

Fig. 6. Comparison between tracks of Dimetropus sp. from the Late Carboniferous Bochum Formation, Ruhr area, Germany, DBM 060003260001 (A) and DBM 060003260003 (B) and Dimetropus leisnerianus (Geinitz, 1863) from the Early Permian Tambach Formation, Thuringian Forest, Germany, MNG 1762 (C). Scale bars 100 mm.

Fig. 4 in Toward the origin of amniotes: Diadectomorph and synapsid footprints from the early Late Carboniferous of Germany

Fig. 4. Graphic comparisons of the trackway pattern and selected parameters of three Ichniotherium ichnospecies. A. Ichniotherium praesidentis from the Bochum Formation, Late Carboniferous, Germany; DBM 060003309001 to DBM 060003309004, part). B–D. Ichniotherium sphaerodactylum from the Tambach Formation, Early Permian, Germany; MB ICV.2 (B), MNG 1351 (C), and GZG 1270 (D). E–G. Ichniotherium cottae from the Tambach Formation, Early Permian, Germany; MNG 10179 (E), UGBL F1 (F), and MNG 1352 (G). H, I. Derived trackway and imprint parameters graphically expressing the mean, minimum, and maximum values. J, K. Relative lengths of the manus and pes imprint digit lengths expressed as a percentage of the length of digit IV. Data source for H–K, see Appendix 2.

Fig. 2 in Toward the origin of amniotes: Diadectomorph and synapsid footprints from the early Late Carboniferous of Germany

Fig. 2. Tetrapod footprints of Ichniotherium praesidentis (Schmidt, 1956) from the Late Carboniferous Bochum Formation, Ruhr area, Germany, represented by the longest and best preserved trackway of the holotype. Photograph and drawing are based on the four−part resin replica (DBM 060003309001 to DBM 060003309004). Note: the first two imprints are erroneously replicated twice and therefore not shown in the outline drawing. Dotted zigzag line links the pes imprints, and dotted track outlines indicate the position of imprints, which are reasoned from the step cycle but not preserved on the cast.

opengpl-3.0-or-laterJul 2024View details →

STILT footprints data set 1

This repository contains the first batch of training data sets of measurement footprints. The footprints are used to train the deep learning model presented in our paper titled "FootNet v1.0: Development of a machine learning emulator of atmospheric transport". Preprint of the manuscript could be accessed at https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1526/. The footprints are provided in Numpy compressed array format, which could be decompressed with Python 3.10.6 and NumPy 1.23.4.

opencc-by-4.0Jan 2018View details →

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the all-sky infrared radiative transfer algorithm

Dataset for AMT-2017-261 by DeSouza-Machado et. al. 1D-variational retrievals of temperature and moisture fields from hyperspectral infrared satellite sounders use cloud-cleared radiances as their observation. These derived observations allow the use of clear-sky only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud-clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that when using multi-layer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2-4 degrees of freedom of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from Numerical Weather Prediction (NWP) models as a first guess, together with a simple cloud representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (typically one for ice and one for water clouds). From these, one run of our fast cloud representation model allows an improvement of the \emph{a-priori} cloud state by comparing the observed and model simulated radiances in the thermal window channels. The retrieval yield is over 90\%, while the degrees of freedom correlate with the observed window channel brightness temperature which itself depends on the cloud optical depth. The cloud representation/scattering package is bench-marked against radiances computed using a Maximum Random Overlap cloud scheme. All-sky infrared radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Center for Medium Range Weather Forecasting (ECMWF) forecast model are used in this paper.

opencc-by-nc-sa-4.0Oct 2018View details →

Green Roofs Footprints for New York City, Assembled from Available Data and Remote Sensing

Summary: The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md</a>). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016. These data are associated with an existing GitHub Repository, <a href="https://github.com/tnc-ny-science/NYC_GreenRoofMapping">https://github.com/tnc-ny-science/NYC_GreenRoofMapping</a>, and as needed and appropriate pending future work, versioned updates will be released here. Terms of Use: The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data. Associated Files: As of this release, the specific files included here are: <ul> <li>GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).</li> <li>GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li>GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li>GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.</li> </ul> Column Information for the datasets: Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below. <ul> <li>fid - Unique identifier</li> <li>bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: <a href="https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh">https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh</a>. Associated metadata for fields from that dataset are available at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md</a>.</li> <li>bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (<a href="http://gis.nyc.gov/taxmap/map.htm">http://gis.nyc.gov/taxmap/map.htm</a>) and PLUTO/MapPLUTO (<a href="https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page">https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page</a>). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.</li> <li>gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li>bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li>prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).</li> <li>cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.</li> <li>doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.</li> <li>heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.</li> <li>feat_code - Code describing the type of building, pulled from the Building Footprint Data.</li> <li>groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.</li> <li>qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'</li> <li>notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.</li> <li>classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)</li> <li>digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)</li> <li>newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)</li> <li>original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.</li> <li>address - Address based on MapPLUTO, joined to the dataset based on bbl.</li> <li>borough - Borough abbreviation pulled from MapPLUTO.</li> <li>ownertype - Owner type field pulled from MapPLUTO.</li> <li>zonedist1 - Zoning District 1 type pulled from MapPLUTO.</li> <li>spdist1 - Special District 1 pulled from MapPLUTO.</li> <li>bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)</li> </ul> For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263): <ul> <li>xcoord - Longitude in decimal degrees.</li> <li>ycoord - Latitude in decimal degrees.</li> </ul> Acknowledgements: This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

Fig. 6. Putative protorosaurid archosauromorph trace Paradoxichnium isp. A, B in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama

Fig. 6. Putative protorosaurid archosauromorph trace Paradoxichnium isp. A, B. Paradoxichnium isp. from Ulbe (Italy), Lopingian. A. MCV 10, right complete manus, note the proximally-positioned digits I and V and the triangular claw impressions. B. MCV 9, complete left manus impression. Note the proximally-positioned digits I and V, the parallel digits II–IV and the triangular claw impressions. C. Paradoxichnium problematicum Müller, 1959, holotype FG 20/1 from Culmitzch (Thuringia, Germany), Lopingian; right (C1) and left (C2) pes-manus couples; note the manual morphology similar to MCV 9. Convex hyporelief, spacing 0.5 mm. Photo (A1, B1), interpretive drawing (A2, B2), false-color depth map (A3, B3), contour lines (A4, B4). Scale bars 10 mm.

Fig. 5 in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama

Fig. 5. Pareiasaurian parareptile trace Pachypes isp. (MCV 3) from Ulbe (Italy), Lopingian. Left manual imprint showing digits I–IV, convex hyporelief. Photo (A), interpretive drawing (B), false-color depth map (C), contour lines (D). Scale bar 10 mm.

Fig. 2 in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama

Fig. 2. Sedimentary structures of Val Gardena Sandstone. Facies association a, fine-grained sandstone showing cross lamination (A) and parallel ripples B). Facies association b, reddish mudstone with paleosols (C), pedogenic veins and nodules in the mudstone (D). Note the gray dolostone strata on the top. Facies association c, gray dolostone strata interbedded in the reddish mudstone (E), invertebrate burrows in the dolostone (F). G. Bellerophon Formation, gray dolostone.

Fig. 8. A in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama

Fig. 8. A. Undetermined track (MCV 14/30) of therapsid synapsid from Cortiana (Italy), Lopingian. Incomplete right manual impression showing digits III–V and deep expulsion rims. Concave epirelief, spacing 1 mm. Photo (A1), interpretive drawing (A2), contour lines (A3), false-color depth map (A4). B. MGP 9/22, interpretive drawing of a complete left manual impression from the Bletterbach Gorge, Dolomites (Italy), Lopingian, after Conti et al. 1977). Scale bar 10 mm.